Overview

Dataset statistics

Number of variables34
Number of observations27754
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 MiB
Average record size in memory280.0 B

Variable types

Categorical21
Numeric13

Alerts

Q10A is highly overall correlated with predictHigh correlation
Q13A is highly overall correlated with predictHigh correlation
Q16A is highly overall correlated with predictHigh correlation
Q17A is highly overall correlated with Q34A and 1 other fieldsHigh correlation
Q21A is highly overall correlated with Q38A and 1 other fieldsHigh correlation
Q24A is highly overall correlated with predictHigh correlation
Q26A is highly overall correlated with predictHigh correlation
Q34A is highly overall correlated with Q17A and 1 other fieldsHigh correlation
Q37A is highly overall correlated with predictHigh correlation
Q38A is highly overall correlated with Q21A and 1 other fieldsHigh correlation
Q3A is highly overall correlated with predictHigh correlation
predict is highly overall correlated with Q10A and 10 other fieldsHigh correlation
gender is highly imbalanced (50.1%)Imbalance
married is highly imbalanced (56.0%)Imbalance
familysize has 632 (2.3%) zerosZeros

Reproduction

Analysis started2024-02-19 11:29:08.117243
Analysis finished2024-02-19 11:30:11.711958
Duration1 minute and 3.59 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Q3A
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
1
9978 
0
8850 
2
5001 
3
3925 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row0
5th row2

Common Values

ValueCountFrequency (%)
1 9978
36.0%
0 8850
31.9%
2 5001
18.0%
3 3925
 
14.1%

Length

2024-02-19T11:30:11.871685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:12.158068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9978
36.0%
0 8850
31.9%
2 5001
18.0%
3 3925
 
14.1%

Most occurring characters

ValueCountFrequency (%)
1 9978
36.0%
0 8850
31.9%
2 5001
18.0%
3 3925
 
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9978
36.0%
0 8850
31.9%
2 5001
18.0%
3 3925
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9978
36.0%
0 8850
31.9%
2 5001
18.0%
3 3925
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9978
36.0%
0 8850
31.9%
2 5001
18.0%
3 3925
 
14.1%

Q5A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
1
9510 
0
6365 
2
5969 
3
5910 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 9510
34.3%
0 6365
22.9%
2 5969
21.5%
3 5910
21.3%

Length

2024-02-19T11:30:12.406674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:12.661231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9510
34.3%
0 6365
22.9%
2 5969
21.5%
3 5910
21.3%

Most occurring characters

ValueCountFrequency (%)
1 9510
34.3%
0 6365
22.9%
2 5969
21.5%
3 5910
21.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9510
34.3%
0 6365
22.9%
2 5969
21.5%
3 5910
21.3%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9510
34.3%
0 6365
22.9%
2 5969
21.5%
3 5910
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9510
34.3%
0 6365
22.9%
2 5969
21.5%
3 5910
21.3%

Q10A
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
0
8226 
1
8094 
3
6331 
2
5103 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 8226
29.6%
1 8094
29.2%
3 6331
22.8%
2 5103
18.4%

Length

2024-02-19T11:30:12.890636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:13.149586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8226
29.6%
1 8094
29.2%
3 6331
22.8%
2 5103
18.4%

Most occurring characters

ValueCountFrequency (%)
0 8226
29.6%
1 8094
29.2%
3 6331
22.8%
2 5103
18.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8226
29.6%
1 8094
29.2%
3 6331
22.8%
2 5103
18.4%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8226
29.6%
1 8094
29.2%
3 6331
22.8%
2 5103
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8226
29.6%
1 8094
29.2%
3 6331
22.8%
2 5103
18.4%

Q13A
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
3
8736 
1
8496 
2
6119 
0
4403 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row1
4th row0
5th row3

Common Values

ValueCountFrequency (%)
3 8736
31.5%
1 8496
30.6%
2 6119
22.0%
0 4403
15.9%

Length

2024-02-19T11:30:13.412227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:13.666778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 8736
31.5%
1 8496
30.6%
2 6119
22.0%
0 4403
15.9%

Most occurring characters

ValueCountFrequency (%)
3 8736
31.5%
1 8496
30.6%
2 6119
22.0%
0 4403
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 8736
31.5%
1 8496
30.6%
2 6119
22.0%
0 4403
15.9%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 8736
31.5%
1 8496
30.6%
2 6119
22.0%
0 4403
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 8736
31.5%
1 8496
30.6%
2 6119
22.0%
0 4403
15.9%

Q16A
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
1
8528 
0
6806 
3
6791 
2
5629 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row0
5th row3

Common Values

ValueCountFrequency (%)
1 8528
30.7%
0 6806
24.5%
3 6791
24.5%
2 5629
20.3%

Length

2024-02-19T11:30:13.898250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:14.164736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 8528
30.7%
0 6806
24.5%
3 6791
24.5%
2 5629
20.3%

Most occurring characters

ValueCountFrequency (%)
1 8528
30.7%
0 6806
24.5%
3 6791
24.5%
2 5629
20.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8528
30.7%
0 6806
24.5%
3 6791
24.5%
2 5629
20.3%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8528
30.7%
0 6806
24.5%
3 6791
24.5%
2 5629
20.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8528
30.7%
0 6806
24.5%
3 6791
24.5%
2 5629
20.3%

Q17A
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
3
8511 
1
7329 
0
6649 
2
5265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row0
5th row2

Common Values

ValueCountFrequency (%)
3 8511
30.7%
1 7329
26.4%
0 6649
24.0%
2 5265
19.0%

Length

2024-02-19T11:30:14.427309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:14.682432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 8511
30.7%
1 7329
26.4%
0 6649
24.0%
2 5265
19.0%

Most occurring characters

ValueCountFrequency (%)
3 8511
30.7%
1 7329
26.4%
0 6649
24.0%
2 5265
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 8511
30.7%
1 7329
26.4%
0 6649
24.0%
2 5265
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 8511
30.7%
1 7329
26.4%
0 6649
24.0%
2 5265
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 8511
30.7%
1 7329
26.4%
0 6649
24.0%
2 5265
19.0%

Q21A
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
0
9743 
1
7597 
3
5965 
2
4449 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row3
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 9743
35.1%
1 7597
27.4%
3 5965
21.5%
2 4449
16.0%

Length

2024-02-19T11:30:14.919044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:16.057473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9743
35.1%
1 7597
27.4%
3 5965
21.5%
2 4449
16.0%

Most occurring characters

ValueCountFrequency (%)
0 9743
35.1%
1 7597
27.4%
3 5965
21.5%
2 4449
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9743
35.1%
1 7597
27.4%
3 5965
21.5%
2 4449
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9743
35.1%
1 7597
27.4%
3 5965
21.5%
2 4449
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9743
35.1%
1 7597
27.4%
3 5965
21.5%
2 4449
16.0%

Q24A
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
1
10015 
0
6553 
2
5853 
3
5333 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row2
4th row0
5th row2

Common Values

ValueCountFrequency (%)
1 10015
36.1%
0 6553
23.6%
2 5853
21.1%
3 5333
19.2%

Length

2024-02-19T11:30:16.325564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:16.629679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 10015
36.1%
0 6553
23.6%
2 5853
21.1%
3 5333
19.2%

Most occurring characters

ValueCountFrequency (%)
1 10015
36.1%
0 6553
23.6%
2 5853
21.1%
3 5333
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10015
36.1%
0 6553
23.6%
2 5853
21.1%
3 5333
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10015
36.1%
0 6553
23.6%
2 5853
21.1%
3 5333
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10015
36.1%
0 6553
23.6%
2 5853
21.1%
3 5333
19.2%

Q26A
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
1
8994 
3
7237 
2
6415 
0
5108 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 8994
32.4%
3 7237
26.1%
2 6415
23.1%
0 5108
18.4%

Length

2024-02-19T11:30:16.869338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:17.130122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 8994
32.4%
3 7237
26.1%
2 6415
23.1%
0 5108
18.4%

Most occurring characters

ValueCountFrequency (%)
1 8994
32.4%
3 7237
26.1%
2 6415
23.1%
0 5108
18.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8994
32.4%
3 7237
26.1%
2 6415
23.1%
0 5108
18.4%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8994
32.4%
3 7237
26.1%
2 6415
23.1%
0 5108
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8994
32.4%
3 7237
26.1%
2 6415
23.1%
0 5108
18.4%

Q31A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
1
10136 
0
7010 
2
5763 
3
4845 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row0
5th row3

Common Values

ValueCountFrequency (%)
1 10136
36.5%
0 7010
25.3%
2 5763
20.8%
3 4845
17.5%

Length

2024-02-19T11:30:17.384892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:17.674345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 10136
36.5%
0 7010
25.3%
2 5763
20.8%
3 4845
17.5%

Most occurring characters

ValueCountFrequency (%)
1 10136
36.5%
0 7010
25.3%
2 5763
20.8%
3 4845
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10136
36.5%
0 7010
25.3%
2 5763
20.8%
3 4845
17.5%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10136
36.5%
0 7010
25.3%
2 5763
20.8%
3 4845
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10136
36.5%
0 7010
25.3%
2 5763
20.8%
3 4845
17.5%

Q34A
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
3
8150 
1
7767 
0
6666 
2
5171 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row0
5th row3

Common Values

ValueCountFrequency (%)
3 8150
29.4%
1 7767
28.0%
0 6666
24.0%
2 5171
18.6%

Length

2024-02-19T11:30:17.924736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:18.277745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 8150
29.4%
1 7767
28.0%
0 6666
24.0%
2 5171
18.6%

Most occurring characters

ValueCountFrequency (%)
3 8150
29.4%
1 7767
28.0%
0 6666
24.0%
2 5171
18.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 8150
29.4%
1 7767
28.0%
0 6666
24.0%
2 5171
18.6%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 8150
29.4%
1 7767
28.0%
0 6666
24.0%
2 5171
18.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 8150
29.4%
1 7767
28.0%
0 6666
24.0%
2 5171
18.6%

Q37A
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
0
8786 
1
8243 
3
5945 
2
4780 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row0
5th row3

Common Values

ValueCountFrequency (%)
0 8786
31.7%
1 8243
29.7%
3 5945
21.4%
2 4780
17.2%

Length

2024-02-19T11:30:18.770246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:19.217629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8786
31.7%
1 8243
29.7%
3 5945
21.4%
2 4780
17.2%

Most occurring characters

ValueCountFrequency (%)
0 8786
31.7%
1 8243
29.7%
3 5945
21.4%
2 4780
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8786
31.7%
1 8243
29.7%
3 5945
21.4%
2 4780
17.2%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8786
31.7%
1 8243
29.7%
3 5945
21.4%
2 4780
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8786
31.7%
1 8243
29.7%
3 5945
21.4%
2 4780
17.2%

Q38A
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
0
9698 
1
7135 
3
6599 
2
4322 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row3
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 9698
34.9%
1 7135
25.7%
3 6599
23.8%
2 4322
15.6%

Length

2024-02-19T11:30:19.689682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:20.097537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9698
34.9%
1 7135
25.7%
3 6599
23.8%
2 4322
15.6%

Most occurring characters

ValueCountFrequency (%)
0 9698
34.9%
1 7135
25.7%
3 6599
23.8%
2 4322
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9698
34.9%
1 7135
25.7%
3 6599
23.8%
2 4322
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9698
34.9%
1 7135
25.7%
3 6599
23.8%
2 4322
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9698
34.9%
1 7135
25.7%
3 6599
23.8%
2 4322
15.6%

Q42A
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
1
9831 
3
6833 
2
6706 
0
4384 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 9831
35.4%
3 6833
24.6%
2 6706
24.2%
0 4384
15.8%

Length

2024-02-19T11:30:20.555746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:21.023421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9831
35.4%
3 6833
24.6%
2 6706
24.2%
0 4384
15.8%

Most occurring characters

ValueCountFrequency (%)
1 9831
35.4%
3 6833
24.6%
2 6706
24.2%
0 4384
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9831
35.4%
3 6833
24.6%
2 6706
24.2%
0 4384
15.8%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9831
35.4%
3 6833
24.6%
2 6706
24.2%
0 4384
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9831
35.4%
3 6833
24.6%
2 6706
24.2%
0 4384
15.8%

TIPI1
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.997622
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:21.356718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8192555
Coefficient of variation (CV)0.45508443
Kurtosis-1.0706209
Mean3.997622
Median Absolute Deviation (MAD)1
Skewness-0.22448922
Sum110950
Variance3.3096906
MonotonicityNot monotonic
2024-02-19T11:30:21.692955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 6540
23.6%
6 4586
16.5%
4 4406
15.9%
1 3690
13.3%
2 3516
12.7%
3 3181
11.5%
7 1835
 
6.6%
ValueCountFrequency (%)
1 3690
13.3%
2 3516
12.7%
3 3181
11.5%
4 4406
15.9%
5 6540
23.6%
6 4586
16.5%
7 1835
 
6.6%
ValueCountFrequency (%)
7 1835
 
6.6%
6 4586
16.5%
5 6540
23.6%
4 4406
15.9%
3 3181
11.5%
2 3516
12.7%
1 3690
13.3%

TIPI2
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2374072
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:22.134330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7603381
Coefficient of variation (CV)0.41542811
Kurtosis-0.84387138
Mean4.2374072
Median Absolute Deviation (MAD)1
Skewness-0.38911459
Sum117605
Variance3.0987902
MonotonicityNot monotonic
2024-02-19T11:30:22.347172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 7611
27.4%
6 4878
17.6%
4 4248
15.3%
3 3042
 
11.0%
1 2881
 
10.4%
2 2875
 
10.4%
7 2219
 
8.0%
ValueCountFrequency (%)
1 2881
 
10.4%
2 2875
 
10.4%
3 3042
 
11.0%
4 4248
15.3%
5 7611
27.4%
6 4878
17.6%
7 2219
 
8.0%
ValueCountFrequency (%)
7 2219
 
8.0%
6 4878
17.6%
5 7611
27.4%
4 4248
15.3%
3 3042
 
11.0%
2 2875
 
10.4%
1 2881
 
10.4%

TIPI3
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9503855
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:22.601581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6650121
Coefficient of variation (CV)0.33633989
Kurtosis-0.22791887
Mean4.9503855
Median Absolute Deviation (MAD)1
Skewness-0.78121787
Sum137393
Variance2.7722654
MonotonicityNot monotonic
2024-02-19T11:30:22.833111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 8037
29.0%
5 6457
23.3%
7 4639
16.7%
4 2994
 
10.8%
3 2529
 
9.1%
2 1752
 
6.3%
1 1346
 
4.8%
ValueCountFrequency (%)
1 1346
 
4.8%
2 1752
 
6.3%
3 2529
 
9.1%
4 2994
 
10.8%
5 6457
23.3%
6 8037
29.0%
7 4639
16.7%
ValueCountFrequency (%)
7 4639
16.7%
6 8037
29.0%
5 6457
23.3%
4 2994
 
10.8%
3 2529
 
9.1%
2 1752
 
6.3%
1 1346
 
4.8%

TIPI4
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1494199
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:23.064532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7736603
Coefficient of variation (CV)0.34443886
Kurtosis-0.15834814
Mean5.1494199
Median Absolute Deviation (MAD)1
Skewness-0.89456071
Sum142917
Variance3.1458709
MonotonicityNot monotonic
2024-02-19T11:30:23.293057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 7607
27.4%
6 6470
23.3%
5 6351
22.9%
4 2085
 
7.5%
2 1840
 
6.6%
3 1836
 
6.6%
1 1565
 
5.6%
ValueCountFrequency (%)
1 1565
 
5.6%
2 1840
 
6.6%
3 1836
 
6.6%
4 2085
 
7.5%
5 6351
22.9%
6 6470
23.3%
7 7607
27.4%
ValueCountFrequency (%)
7 7607
27.4%
6 6470
23.3%
5 6351
22.9%
4 2085
 
7.5%
3 1836
 
6.6%
2 1840
 
6.6%
1 1565
 
5.6%

TIPI5
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1371334
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:23.535621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5732522
Coefficient of variation (CV)0.30625098
Kurtosis0.08126505
Mean5.1371334
Median Absolute Deviation (MAD)1
Skewness-0.83138726
Sum142576
Variance2.4751223
MonotonicityNot monotonic
2024-02-19T11:30:23.770556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 7545
27.2%
5 6617
23.8%
7 5783
20.8%
4 3500
12.6%
3 2055
 
7.4%
2 1321
 
4.8%
1 933
 
3.4%
ValueCountFrequency (%)
1 933
 
3.4%
2 1321
 
4.8%
3 2055
 
7.4%
4 3500
12.6%
5 6617
23.8%
6 7545
27.2%
7 5783
20.8%
ValueCountFrequency (%)
7 5783
20.8%
6 7545
27.2%
5 6617
23.8%
4 3500
12.6%
3 2055
 
7.4%
2 1321
 
4.8%
1 933
 
3.4%

TIPI6
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8463285
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:24.004706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8558625
Coefficient of variation (CV)0.38294195
Kurtosis-0.69800175
Mean4.8463285
Median Absolute Deviation (MAD)1
Skewness-0.59834383
Sum134505
Variance3.4442255
MonotonicityNot monotonic
2024-02-19T11:30:24.212708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 6565
23.7%
5 5510
19.9%
6 5494
19.8%
4 3568
12.9%
3 2539
 
9.1%
2 2069
 
7.5%
1 2009
 
7.2%
ValueCountFrequency (%)
1 2009
 
7.2%
2 2069
 
7.5%
3 2539
 
9.1%
4 3568
12.9%
5 5510
19.9%
6 5494
19.8%
7 6565
23.7%
ValueCountFrequency (%)
7 6565
23.7%
6 5494
19.8%
5 5510
19.9%
4 3568
12.9%
3 2539
 
9.1%
2 2069
 
7.5%
1 2009
 
7.2%

TIPI7
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4436117
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:24.439025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4285901
Coefficient of variation (CV)0.26243423
Kurtosis0.78162478
Mean5.4436117
Median Absolute Deviation (MAD)1
Skewness-1.0323306
Sum151082
Variance2.0408695
MonotonicityNot monotonic
2024-02-19T11:30:24.638996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 8402
30.3%
7 7205
26.0%
5 6367
22.9%
4 2979
 
10.7%
3 1433
 
5.2%
2 817
 
2.9%
1 551
 
2.0%
ValueCountFrequency (%)
1 551
 
2.0%
2 817
 
2.9%
3 1433
 
5.2%
4 2979
 
10.7%
5 6367
22.9%
6 8402
30.3%
7 7205
26.0%
ValueCountFrequency (%)
7 7205
26.0%
6 8402
30.3%
5 6367
22.9%
4 2979
 
10.7%
3 1433
 
5.2%
2 817
 
2.9%
1 551
 
2.0%

TIPI8
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2796354
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:24.876690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8841597
Coefficient of variation (CV)0.44026173
Kurtosis-1.0396629
Mean4.2796354
Median Absolute Deviation (MAD)1
Skewness-0.28752309
Sum118777
Variance3.5500576
MonotonicityNot monotonic
2024-02-19T11:30:25.101117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 6963
25.1%
6 4316
15.6%
7 3669
13.2%
2 3300
11.9%
4 3267
11.8%
3 3238
11.7%
1 3001
10.8%
ValueCountFrequency (%)
1 3001
10.8%
2 3300
11.9%
3 3238
11.7%
4 3267
11.8%
5 6963
25.1%
6 4316
15.6%
7 3669
13.2%
ValueCountFrequency (%)
7 3669
13.2%
6 4316
15.6%
5 6963
25.1%
4 3267
11.8%
3 3238
11.7%
2 3300
11.9%
1 3001
10.8%

TIPI9
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8747568
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:25.348688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7643447
Coefficient of variation (CV)0.45534334
Kurtosis-0.98662811
Mean3.8747568
Median Absolute Deviation (MAD)1
Skewness0.0073536102
Sum107540
Variance3.1129123
MonotonicityNot monotonic
2024-02-19T11:30:25.581446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 5331
19.2%
5 4784
17.2%
3 4694
16.9%
2 3946
14.2%
6 3885
14.0%
1 3131
11.3%
7 1983
 
7.1%
ValueCountFrequency (%)
1 3131
11.3%
2 3946
14.2%
3 4694
16.9%
4 5331
19.2%
5 4784
17.2%
6 3885
14.0%
7 1983
 
7.1%
ValueCountFrequency (%)
7 1983
 
7.1%
6 3885
14.0%
5 4784
17.2%
4 5331
19.2%
3 4694
16.9%
2 3946
14.2%
1 3131
11.3%

TIPI10
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8442026
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:25.830837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7897881
Coefficient of variation (CV)0.46558109
Kurtosis-0.93487613
Mean3.8442026
Median Absolute Deviation (MAD)1
Skewness0.042281562
Sum106692
Variance3.2033413
MonotonicityNot monotonic
2024-02-19T11:30:26.033957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 5887
21.2%
5 4856
17.5%
3 4293
15.5%
2 3828
13.8%
1 3487
12.6%
6 2979
10.7%
7 2424
8.7%
ValueCountFrequency (%)
1 3487
12.6%
2 3828
13.8%
3 4293
15.5%
4 5887
21.2%
5 4856
17.5%
6 2979
10.7%
7 2424
8.7%
ValueCountFrequency (%)
7 2424
8.7%
6 2979
10.7%
5 4856
17.5%
4 5887
21.2%
3 4293
15.5%
2 3828
13.8%
1 3487
12.6%

education
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
3
14582 
2
7855 
4
4612 
1
 
705

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row2
4th row4
5th row2

Common Values

ValueCountFrequency (%)
3 14582
52.5%
2 7855
28.3%
4 4612
 
16.6%
1 705
 
2.5%

Length

2024-02-19T11:30:26.332933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:26.594325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 14582
52.5%
2 7855
28.3%
4 4612
 
16.6%
1 705
 
2.5%

Most occurring characters

ValueCountFrequency (%)
3 14582
52.5%
2 7855
28.3%
4 4612
 
16.6%
1 705
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 14582
52.5%
2 7855
28.3%
4 4612
 
16.6%
1 705
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 14582
52.5%
2 7855
28.3%
4 4612
 
16.6%
1 705
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 14582
52.5%
2 7855
28.3%
4 4612
 
16.6%
1 705
 
2.5%

urban
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
3
12599 
2
9089 
1
6066 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 12599
45.4%
2 9089
32.7%
1 6066
21.9%

Length

2024-02-19T11:30:26.842487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:27.096187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 12599
45.4%
2 9089
32.7%
1 6066
21.9%

Most occurring characters

ValueCountFrequency (%)
3 12599
45.4%
2 9089
32.7%
1 6066
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 12599
45.4%
2 9089
32.7%
1 6066
21.9%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 12599
45.4%
2 9089
32.7%
1 6066
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 12599
45.4%
2 9089
32.7%
1 6066
21.9%

gender
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
2
21862 
1
5692 
3
 
200

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 21862
78.8%
1 5692
 
20.5%
3 200
 
0.7%

Length

2024-02-19T11:30:27.324553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:27.579318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 21862
78.8%
1 5692
 
20.5%
3 200
 
0.7%

Most occurring characters

ValueCountFrequency (%)
2 21862
78.8%
1 5692
 
20.5%
3 200
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 21862
78.8%
1 5692
 
20.5%
3 200
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 21862
78.8%
1 5692
 
20.5%
3 200
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 21862
78.8%
1 5692
 
20.5%
3 200
 
0.7%

religion
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2458024
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:27.789358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median10
Q310
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1429458
Coefficient of variation (CV)0.38115706
Kurtosis0.12973774
Mean8.2458024
Median Absolute Deviation (MAD)0
Skewness-1.2912305
Sum228854
Variance9.8781082
MonotonicityNot monotonic
2024-02-19T11:30:28.032478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 18623
67.1%
2 1765
 
6.4%
4 1748
 
6.3%
1 1658
 
6.0%
7 1052
 
3.8%
6 987
 
3.6%
12 871
 
3.1%
8 540
 
1.9%
3 351
 
1.3%
9 70
 
0.3%
Other values (2) 89
 
0.3%
ValueCountFrequency (%)
1 1658
 
6.0%
2 1765
 
6.4%
3 351
 
1.3%
4 1748
 
6.3%
5 46
 
0.2%
6 987
 
3.6%
7 1052
 
3.8%
8 540
 
1.9%
9 70
 
0.3%
10 18623
67.1%
ValueCountFrequency (%)
12 871
 
3.1%
11 43
 
0.2%
10 18623
67.1%
9 70
 
0.3%
8 540
 
1.9%
7 1052
 
3.8%
6 987
 
3.6%
5 46
 
0.2%
4 1748
 
6.3%
3 351
 
1.3%

orientation
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
1
19869 
2
2757 
5
2439 
4
 
1382
3
 
1307

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 19869
71.6%
2 2757
 
9.9%
5 2439
 
8.8%
4 1382
 
5.0%
3 1307
 
4.7%

Length

2024-02-19T11:30:28.285833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:28.548014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 19869
71.6%
2 2757
 
9.9%
5 2439
 
8.8%
4 1382
 
5.0%
3 1307
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 19869
71.6%
2 2757
 
9.9%
5 2439
 
8.8%
4 1382
 
5.0%
3 1307
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 19869
71.6%
2 2757
 
9.9%
5 2439
 
8.8%
4 1382
 
5.0%
3 1307
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 19869
71.6%
2 2757
 
9.9%
5 2439
 
8.8%
4 1382
 
5.0%
3 1307
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19869
71.6%
2 2757
 
9.9%
5 2439
 
8.8%
4 1382
 
5.0%
3 1307
 
4.7%

race
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.672191
Minimum10
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:28.759002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median10
Q360
95-th percentile70
Maximum70
Range60
Interquartile range (IQR)50

Descriptive statistics

Standard deviation24.831536
Coefficient of variation (CV)0.93098972
Kurtosis-1.1586566
Mean26.672191
Median Absolute Deviation (MAD)0
Skewness0.8732769
Sum740260
Variance616.60518
MonotonicityNot monotonic
2024-02-19T11:30:28.968550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 18782
67.7%
60 5005
 
18.0%
70 3342
 
12.0%
30 310
 
1.1%
20 225
 
0.8%
50 80
 
0.3%
40 10
 
< 0.1%
ValueCountFrequency (%)
10 18782
67.7%
20 225
 
0.8%
30 310
 
1.1%
40 10
 
< 0.1%
50 80
 
0.3%
60 5005
 
18.0%
70 3342
 
12.0%
ValueCountFrequency (%)
70 3342
 
12.0%
60 5005
 
18.0%
50 80
 
0.3%
40 10
 
< 0.1%
30 310
 
1.1%
20 225
 
0.8%
10 18782
67.7%

married
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
1
23686 
2
3370 
3
 
698

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23686
85.3%
2 3370
 
12.1%
3 698
 
2.5%

Length

2024-02-19T11:30:29.245154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:29.523541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 23686
85.3%
2 3370
 
12.1%
3 698
 
2.5%

Most occurring characters

ValueCountFrequency (%)
1 23686
85.3%
2 3370
 
12.1%
3 698
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23686
85.3%
2 3370
 
12.1%
3 698
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23686
85.3%
2 3370
 
12.1%
3 698
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23686
85.3%
2 3370
 
12.1%
3 698
 
2.5%

familysize
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7097716
Minimum0
Maximum15
Zeros632
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-02-19T11:30:29.728663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9270672
Coefficient of variation (CV)0.51945711
Kurtosis1.7202706
Mean3.7097716
Median Absolute Deviation (MAD)1
Skewness0.9190399
Sum102961
Variance3.7135881
MonotonicityNot monotonic
2024-02-19T11:30:29.982459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 6268
22.6%
4 5731
20.6%
2 5433
19.6%
5 3836
13.8%
6 1994
 
7.2%
1 1688
 
6.1%
7 1029
 
3.7%
0 632
 
2.3%
8 548
 
2.0%
9 269
 
1.0%
Other values (6) 326
 
1.2%
ValueCountFrequency (%)
0 632
 
2.3%
1 1688
 
6.1%
2 5433
19.6%
3 6268
22.6%
4 5731
20.6%
5 3836
13.8%
6 1994
 
7.2%
7 1029
 
3.7%
8 548
 
2.0%
9 269
 
1.0%
ValueCountFrequency (%)
15 3
 
< 0.1%
14 8
 
< 0.1%
13 15
 
0.1%
12 39
 
0.1%
11 97
 
0.3%
10 164
 
0.6%
9 269
 
1.0%
8 548
 
2.0%
7 1029
3.7%
6 1994
7.2%

age_group
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
4
13094 
3
11353 
5
1486 
2
 
1073
6
 
748

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters27754
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row4
5th row2

Common Values

ValueCountFrequency (%)
4 13094
47.2%
3 11353
40.9%
5 1486
 
5.4%
2 1073
 
3.9%
6 748
 
2.7%

Length

2024-02-19T11:30:30.252650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:30.516645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 13094
47.2%
3 11353
40.9%
5 1486
 
5.4%
2 1073
 
3.9%
6 748
 
2.7%

Most occurring characters

ValueCountFrequency (%)
4 13094
47.2%
3 11353
40.9%
5 1486
 
5.4%
2 1073
 
3.9%
6 748
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 13094
47.2%
3 11353
40.9%
5 1486
 
5.4%
2 1073
 
3.9%
6 748
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 27754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 13094
47.2%
3 11353
40.9%
5 1486
 
5.4%
2 1073
 
3.9%
6 748
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 13094
47.2%
3 11353
40.9%
5 1486
 
5.4%
2 1073
 
3.9%
6 748
 
2.7%

predict
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
Extremely Severe
8005 
Normal
6991 
Moderate
5277 
Severe
4558 
Mild
2923 

Length

Max length16
Median length8
Mean length9.0539021
Min length4

Characters and Unicode

Total characters251282
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowExtremely Severe
3rd rowExtremely Severe
4th rowNormal
5th rowExtremely Severe

Common Values

ValueCountFrequency (%)
Extremely Severe 8005
28.8%
Normal 6991
25.2%
Moderate 5277
19.0%
Severe 4558
16.4%
Mild 2923
 
10.5%

Length

2024-02-19T11:30:30.781266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T11:30:31.092593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
severe 12563
35.1%
extremely 8005
22.4%
normal 6991
19.6%
moderate 5277
14.8%
mild 2923
 
8.2%

Most occurring characters

ValueCountFrequency (%)
e 64253
25.6%
r 32836
13.1%
l 17919
 
7.1%
m 14996
 
6.0%
t 13282
 
5.3%
S 12563
 
5.0%
v 12563
 
5.0%
o 12268
 
4.9%
a 12268
 
4.9%
d 8200
 
3.3%
Other values (7) 50134
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 207518
82.6%
Uppercase Letter 35759
 
14.2%
Space Separator 8005
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 64253
31.0%
r 32836
15.8%
l 17919
 
8.6%
m 14996
 
7.2%
t 13282
 
6.4%
v 12563
 
6.1%
o 12268
 
5.9%
a 12268
 
5.9%
d 8200
 
4.0%
x 8005
 
3.9%
Other values (2) 10928
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
S 12563
35.1%
M 8200
22.9%
E 8005
22.4%
N 6991
19.6%
Space Separator
ValueCountFrequency (%)
8005
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 243277
96.8%
Common 8005
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 64253
26.4%
r 32836
13.5%
l 17919
 
7.4%
m 14996
 
6.2%
t 13282
 
5.5%
S 12563
 
5.2%
v 12563
 
5.2%
o 12268
 
5.0%
a 12268
 
5.0%
d 8200
 
3.4%
Other values (6) 42129
17.3%
Common
ValueCountFrequency (%)
8005
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 251282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 64253
25.6%
r 32836
13.1%
l 17919
 
7.1%
m 14996
 
6.0%
t 13282
 
5.3%
S 12563
 
5.0%
v 12563
 
5.0%
o 12268
 
4.9%
a 12268
 
4.9%
d 8200
 
3.3%
Other values (7) 50134
20.0%

Interactions

2024-02-19T11:30:05.583356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:18.636790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:22.096028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:27.249182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:30.679137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:34.120173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:38.108341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:42.264134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:45.676263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:49.984179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:54.622875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:58.111005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:01.653537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:06.040443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:18.898312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:22.360219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:27.523232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:30.937388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:34.386666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:38.427242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:42.532467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:45.940815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:50.247584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:54.892045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:58.368110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:01.939992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:06.435217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:19.176560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:22.643340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:27.784722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:31.193676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:34.633667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:38.792747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:42.792845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:46.233119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:50.587362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:55.165666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:58.625159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:02.195889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:06.875863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:19.441210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:23.000154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:28.032359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:31.457056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:34.901148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:39.165903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:43.051214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:46.500007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:51.006553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:55.452621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:58.910473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:02.460011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:07.324963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:19.713364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:23.393276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:28.283045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:31.737768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:35.147670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:39.564742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:43.323433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:46.762904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:51.423247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:55.723156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:59.194214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:02.725189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:07.726128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:19.984398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:23.782859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:28.547619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:31.996086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:35.402770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:39.980105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:43.578868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:47.785262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:51.764517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:55.985274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:59.473024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:02.998746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:08.124080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:20.279218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:24.186015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:28.807741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:32.254915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:35.650991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:40.377723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:43.839272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:48.049049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:52.185465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:56.248984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:59.748184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:03.270360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:08.381644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:20.535883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:24.597576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:29.072150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:32.517591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:35.934573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:40.642385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:44.099128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:48.338245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:52.593391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:56.513970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:00.033691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:03.540392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:08.645853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:20.796374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:25.799706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:29.334535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:32.795503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:36.193308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:40.907492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:44.357088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:48.606118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:52.961157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:56.788331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:00.306262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:03.810771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:08.909388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:21.053913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:26.200852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:29.599167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:33.046337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:36.504553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:41.187165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:44.612811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:48.869304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:53.237790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:57.048536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:00.589157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:04.076156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:09.184385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:21.327302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:26.498759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:29.867844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:33.314502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:36.900436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:41.463342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:44.881507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:49.141703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:53.618235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:57.305836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:00.879491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:04.383189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:09.443247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:21.576213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:26.739075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:30.125222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:33.564771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:37.252914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:41.722707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:45.149684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:49.438067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:53.998298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:57.551918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:01.132083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:04.733492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:09.718013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:21.834491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:26.982974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:30.395749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:33.840255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:37.670085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:41.993646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:45.411192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:49.708315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:54.350159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:29:57.835090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:01.388601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-19T11:30:05.148034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-19T11:30:31.388620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Q10AQ13AQ16AQ17AQ21AQ24AQ26AQ31AQ34AQ37AQ38AQ3AQ42AQ5ATIPI1TIPI10TIPI2TIPI3TIPI4TIPI5TIPI6TIPI7TIPI8TIPI9age_groupeducationfamilysizegendermarriedorientationpredictracereligionurban
Q10A1.0000.3960.4250.4230.4680.4010.3800.3940.4270.4970.4640.4140.3260.390-0.2090.1240.119-0.1820.337-0.1970.186-0.0410.202-0.3580.0580.053-0.0220.0270.0860.0440.5580.003-0.0060.019
Q13A0.3961.0000.4000.4160.4260.3870.4930.3690.4290.3680.4230.4000.3150.390-0.1630.0790.153-0.1440.445-0.1640.1830.0060.183-0.4160.0690.053-0.0270.0560.0740.0480.5400.0050.0000.026
Q16A0.4250.4001.0000.3760.4040.4450.3780.4360.3820.3960.4030.4080.3430.388-0.1810.1180.133-0.1630.340-0.1680.187-0.0340.203-0.3420.0620.041-0.0000.0320.0850.0440.529-0.0150.0100.015
Q17A0.4230.4160.3761.0000.4720.3500.3890.3420.5480.4090.4610.3730.2990.360-0.1960.1350.137-0.1910.403-0.1840.190-0.0170.228-0.3940.0970.071-0.0060.0500.1140.0450.537-0.0440.0480.026
Q21A0.4680.4260.4040.4721.0000.3770.3950.3730.4760.4560.5710.4060.3000.382-0.1910.1130.142-0.1710.373-0.1840.187-0.0510.207-0.3820.0760.061-0.0180.0260.1010.0550.572-0.0130.0050.024
Q24A0.4010.3870.4450.3500.3771.0000.3740.4410.3570.3720.3730.4250.3430.390-0.1920.1030.128-0.1510.345-0.1770.195-0.0360.180-0.3500.0370.030-0.0300.0240.0520.0420.5030.021-0.0280.000
Q26A0.3800.4930.3780.3890.3950.3741.0000.3670.4070.3530.3890.3810.3200.387-0.1610.0740.137-0.1440.405-0.1480.1770.0240.180-0.3890.0560.046-0.0390.0450.0800.0400.5090.018-0.0270.021
Q31A0.3940.3690.4360.3420.3730.4410.3671.0000.3530.3730.3640.4170.3560.390-0.2210.1050.124-0.1600.319-0.1710.204-0.0350.186-0.3390.0360.034-0.0290.0220.0560.0310.4950.020-0.0370.000
Q34A0.4270.4290.3820.5480.4760.3570.4070.3531.0000.4110.4700.3810.3090.371-0.1910.1290.141-0.1950.405-0.1870.187-0.0140.231-0.4010.1010.069-0.0010.0510.1200.0450.550-0.0380.0400.024
Q37A0.4970.3680.3960.4090.4560.3720.3530.3730.4111.0000.4530.3960.3060.362-0.2070.1360.118-0.1830.337-0.1970.191-0.0510.209-0.3490.0530.043-0.0060.0190.0890.0380.525-0.0200.0150.021
Q38A0.4640.4230.4030.4610.5710.3730.3890.3640.4700.4531.0000.4010.2970.372-0.1980.1100.142-0.1710.367-0.1810.185-0.0560.206-0.3770.0770.061-0.0210.0260.1010.0570.566-0.001-0.0090.029
Q3A0.4140.4000.4080.3730.4060.4250.3810.4170.3810.3960.4011.0000.3180.381-0.1930.1020.131-0.1570.366-0.1880.189-0.0450.180-0.3790.0490.043-0.0360.0230.0630.0390.5160.002-0.0200.025
Q42A0.3260.3150.3430.2990.3000.3430.3200.3560.3090.3060.2970.3181.0000.399-0.2050.0640.122-0.2200.309-0.1580.174-0.0150.232-0.3360.0360.047-0.0820.0380.0610.0440.4140.089-0.1070.022
Q5A0.3900.3900.3880.3600.3820.3900.3870.3900.3710.3620.3720.3810.3991.000-0.1950.0660.133-0.1860.362-0.1610.186-0.0100.201-0.3690.0390.049-0.0700.0360.0590.0470.4980.068-0.0780.000
TIPI1-0.209-0.163-0.181-0.196-0.191-0.192-0.161-0.221-0.191-0.207-0.198-0.193-0.205-0.1951.000-0.0710.1340.194-0.0920.313-0.3760.159-0.0220.1940.0460.0330.0620.0580.0460.0330.135-0.0700.1020.036
TIPI100.1240.0790.1180.1350.1130.1030.0740.1050.1290.1360.1100.1020.0640.066-0.0711.0000.054-0.0750.138-0.1880.111-0.0440.202-0.0330.0840.0440.1340.0670.0880.0420.084-0.1690.1880.030
TIPI20.1190.1530.1330.1370.1420.1280.1370.1240.1410.1180.1420.1310.1220.1330.1340.0541.0000.0160.2570.011-0.007-0.0680.131-0.1860.0690.034-0.0050.0140.0610.0230.098-0.0160.0120.032
TIPI3-0.182-0.144-0.163-0.191-0.171-0.151-0.144-0.160-0.195-0.183-0.171-0.157-0.220-0.1860.194-0.0750.0161.000-0.0930.249-0.0040.158-0.3230.2730.0630.0690.0280.0470.0710.0180.1200.0090.0150.032
TIPI40.3370.4450.3400.4030.3730.3450.4050.3190.4050.3370.3670.3660.3090.362-0.0920.1380.257-0.0931.000-0.1700.1940.0620.220-0.4650.0760.052-0.0100.1160.0800.0370.240-0.0240.0460.036
TIPI5-0.197-0.164-0.168-0.184-0.184-0.177-0.148-0.171-0.187-0.197-0.181-0.188-0.158-0.1610.313-0.1880.0110.249-0.1701.000-0.1310.177-0.0550.2600.0520.057-0.0080.0450.0460.0210.1220.022-0.0310.024
TIPI60.1860.1830.1870.1900.1870.1950.1770.2040.1870.1910.1850.1890.1740.186-0.3760.111-0.007-0.0040.194-0.1311.0000.0810.066-0.0460.0280.028-0.0270.0450.0580.0200.1230.021-0.0540.024
TIPI7-0.0410.006-0.034-0.017-0.051-0.0360.024-0.035-0.014-0.051-0.056-0.045-0.015-0.0100.159-0.044-0.0680.1580.0620.1770.0811.000-0.0000.1090.0380.0300.0240.0700.0400.0220.046-0.0110.0280.014
TIPI80.2020.1830.2030.2280.2070.1800.1800.1860.2310.2090.2060.1800.2320.201-0.0220.2020.131-0.3230.220-0.0550.066-0.0001.000-0.1840.0850.0740.0300.0200.0940.0290.139-0.0590.0770.016
TIPI9-0.358-0.416-0.342-0.394-0.382-0.350-0.389-0.339-0.401-0.349-0.377-0.379-0.336-0.3690.194-0.033-0.1860.273-0.4650.260-0.0460.109-0.1841.0000.0650.0610.0560.0720.0660.0390.246-0.0440.0420.030
age_group0.0580.0690.0620.0970.0760.0370.0560.0360.1010.0530.0770.0490.0360.0390.0460.0840.0690.0630.0760.0520.0280.0380.0850.0651.0000.448-0.0010.0830.4170.0460.0640.130-0.1140.061
education0.0530.0530.0410.0710.0610.0300.0460.0340.0690.0430.0610.0430.0470.0490.0330.0440.0340.0690.0520.0570.0280.0300.0740.0610.4481.0000.0440.0340.1780.0390.061-0.0080.0090.032
familysize-0.022-0.027-0.000-0.006-0.018-0.030-0.039-0.029-0.001-0.006-0.021-0.036-0.082-0.0700.0620.134-0.0050.028-0.010-0.008-0.0270.0240.0300.056-0.0010.0441.0000.0800.0480.0460.016-0.2450.3550.093
gender0.0270.0560.0320.0500.0260.0240.0450.0220.0510.0190.0260.0230.0380.0360.0580.0670.0140.0470.1160.0450.0450.0700.0200.0720.0830.0340.0801.0000.0250.1090.034-0.0990.1600.051
married0.0860.0740.0850.1140.1010.0520.0800.0560.1200.0890.1010.0630.0610.0590.0460.0880.0610.0710.0800.0460.0580.0400.0940.0660.4170.1780.0480.0251.0000.0420.0990.149-0.1130.033
orientation0.0440.0480.0440.0450.0550.0420.0400.0310.0450.0380.0570.0390.0440.0470.0330.0420.0230.0180.0370.0210.0200.0220.0290.0390.0460.0390.0460.1090.0421.0000.0440.0310.0240.056
predict0.5580.5400.5290.5370.5720.5030.5090.4950.5500.5250.5660.5160.4140.4980.1350.0840.0980.1200.2400.1220.1230.0460.1390.2460.0640.0610.0160.0340.0990.0441.000-0.0060.0170.020
race0.0030.005-0.015-0.044-0.0130.0210.0180.020-0.038-0.020-0.0010.0020.0890.068-0.070-0.169-0.0160.009-0.0240.0220.021-0.011-0.059-0.0440.130-0.008-0.245-0.0990.1490.031-0.0061.000-0.3750.077
religion-0.0060.0000.0100.0480.005-0.028-0.027-0.0370.0400.015-0.009-0.020-0.107-0.0780.1020.1880.0120.0150.046-0.031-0.0540.0280.0770.042-0.1140.0090.3550.160-0.1130.0240.017-0.3751.0000.080
urban0.0190.0260.0150.0260.0240.0000.0210.0000.0240.0210.0290.0250.0220.0000.0360.0300.0320.0320.0360.0240.0240.0140.0160.0300.0610.0320.0930.0510.0330.0560.0200.0770.0801.000

Missing values

2024-02-19T11:30:10.217049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-19T11:30:11.217544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Q3AQ5AQ10AQ13AQ16AQ17AQ21AQ24AQ26AQ31AQ34AQ37AQ38AQ42ATIPI1TIPI2TIPI3TIPI4TIPI5TIPI6TIPI7TIPI8TIPI9TIPI10educationurbangenderreligionorientationracemarriedfamilysizeage_grouppredict
01220120012310111746461611324570152Moderate
123232321323222253655563332210110143Extremely Severe
21331313213233225653263552327260143Extremely Severe
30200000010000176453263524222260124Normal
422233222333323145757671423112270142Extremely Severe
53333333333333317575712171122260133Extremely Severe
60022222120131151465576213321160123Moderate
702032302010002525526766242112160154Moderate
80000000000000035616532723211160324Normal
91002020130000217571654132227160123Mild
Q3AQ5AQ10AQ13AQ16AQ17AQ21AQ24AQ26AQ31AQ34AQ37AQ38AQ42ATIPI1TIPI2TIPI3TIPI4TIPI5TIPI6TIPI7TIPI8TIPI9TIPI10educationurbangenderreligionorientationracemarriedfamilysizeage_grouppredict
2834112212211112111545655654433210410164Moderate
2834201111011110112326246635623112170124Mild
2834321232212311222365676777733210110264Severe
283440101010010200141777771113224170123Normal
2834533333333333333155736643622210120123Extremely Severe
283460101100110000165676465534124160134Normal
2834722333233333233455746474432110110143Extremely Severe
283481001000101000066756361543227130235Normal
283491203011121101216573535343226160123Moderate
2835013023331112133623563551233210110144Severe